{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What's going on with ROCAUC for Binary Classification?\n",
    "\n",
    "We've identified [a bug in ROCAUC](https://github.com/DistrictDataLabs/yellowbrick/issues/252):\n",
    "\n",
    "```\n",
    "======================================================================\n",
    "ERROR: Test ROCAUC with a binary classifier\n",
    "----------------------------------------------------------------------\n",
    "Traceback (most recent call last):\n",
    "  File \"/Users/benjamin/Repos/ddl/yellowbrick/tests/test_classifier/test_rocauc.py\", line 110, in test_binary_rocauc\n",
    "    s = visualizer.score(X_test, y_test)\n",
    "  File \"/Users/benjamin/Repos/ddl/yellowbrick/yellowbrick/classifier/rocauc.py\", line 171, in score\n",
    "    self.fpr[i], self.tpr[i], _ = roc_curve(y, y_pred[:,i], pos_label=c)\n",
    "IndexError: too many indices for array\n",
    "```\n",
    "\n",
    "Let's see if we can figure out where it's getting triggered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys \n",
    "\n",
    "# Modify the path \n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import pandas as pd\n",
    "import yellowbrick as yb\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from yellowbrick.classifier import ROCAUC\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "occupancy = pd.read_csv('data/occupancy/occupancy.csv')\n",
    "features = [\n",
    "    \"temperature\", \"relative humidity\", \"light\", \"C02\", \"humidity\"\n",
    "]\n",
    "classes = [\"unoccupied\", \"occupied\"]\n",
    "X = occupancy[features]\n",
    "y = occupancy['occupancy']\n",
    "\n",
    "# Create the train and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binary Classification with 1D Coefficients or Feature Importances\n",
    "\n",
    "When the function has 1D coefficients, we don't seem to have a problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifiers = [\n",
    "    AdaBoostClassifier(),\n",
    "    MLPClassifier(),\n",
    "    DecisionTreeClassifier(),\n",
    "    QuadraticDiscriminantAnalysis(),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "   ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for classifier in classifiers:\n",
    "    oz = ROCAUC(classifier)\n",
    "    oz.fit(X_train, y_train)\n",
    "    oz.score(X_test, y_test)\n",
    "    g = oz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good; everything works!\n",
    "\n",
    "## Binary Classification with Multidimensional Coefficients or Feature Importances\n",
    "\n",
    "What about classification with estimators that have multidimensional coefficients? Thanks to ZJ Poh for identifying these in [this PR](https://github.com/DistrictDataLabs/yellowbrick/pull/510)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC, NuSVC, SVC\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB, MultinomialNB\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of these generate the `IndexError: too many indices for array` error, but not all!\n",
    "\n",
    "These are the ones that seem to work: `BernoulliNB()`, `MultinomialNB()`, `LogisticRegression()`, and `LogisticRegressionCV()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1YjHULH4TVQW7887fqKrgUBW2/VoRgCKeKTx5N5OqpFneoYuluJmc+rfwbJU/MOAkyZb6e+VlNqHe2Qa/xqRmqln8BkU9k3GqCioKNrsTg9FIgtXMyejUIYeLeydSwf/2Xz+f+tf2PH4lEIeq4GF0ULvEjTuv/7UtVeD3m36uTDUCY/AwOtJsI1WFm8me/HHL17XNA7yS/7YssDmNRFwvAoCfh5Xyhe/JdM/yfrvhT4rDiKKkbs97pS5P4Wp8IVemcoVv42l0oKKgquC88/Ml2kxcTUgdVc/Xw4qfhxVVTf35Vf76P/rOPEZFxc/D6sqjquC8s8wkmxnHneUa1WRMJnOa15+tVobhzWthUIx3cqpylPUBREdH065dOwIDA/nyyy+JjIx01b8H4dZe8VFRUQwZMiRNBzmbzUa7du1Yu3YthQoVolu3bixYsCDdiF/3SklJ4eTJk2y+Cp/9eIEWj/xBw3J/ur7mYpMtXE/0oqxfPAZFJdluvPMlqOJpcnA1vhB/3PJDVeGRorcoVigJm8OAQ00tIBajgySbiZ/+DERVFQK9EwkKiMWhKtjvFBmj4sRoUNl/oTRJdhMmg5OnKkah8tc8CipGg8qPl4u7/ribV7qIn6cVu1OBO4lNBicXb/ly9NJfP7MKxCZ7cOvOH2lx70Q8TXdH5PrrS89qN7r+SH0sVvw9UzLcZpfifFBRMBmcFPfOeGcjJsmTZLvJtT6n3YrZbEYFrsQlAVDYy5e4lNRMfh4peJlSv8xsTgM2h+HOF2nqH/zd7WQxOf76ofirIKXc87l4mP5W/O/Ma3f+9bmYDQ7u/U64+4uq3vO5GBQnBuXuK2mLg8NpcL3zr3nSSv3yS32foqhpvlnTvkO5Z+o/+6L6+x3kRPa99FgFZnbIeqz78PDwhxoTX2SfbOOc43A4XEMcL1u2jNq1a1OrVi1OnjxJrVq18PDweKDl5VqLffPmzSQmJtK1a1dGjBjBq6++iqqqdOrU6b5F/V6LD//KlQQbNYrH3CkPEJdiZlPEI/xxp4WTld3nyz78D/E34Zezzh15s/BDLfvu3vP9xFstxFvvXyDsTgOXb/tka31Wq8lVcBSDd7a/RMWDkS9EIQSk7uSPGzeOqKgoVqxYgaIo9OnTB0ht0D4stxb2smXLulrr997pqHnz5jRv3vyhljn0qRqUKXyJxBQbhTwK80qTEfxfi6zfJ7ImBUcIIXKPyWQiIiKCK1eucOPGjRy7OkxXgxSXKezFzA4hqKqKiorTmfG5XCGEECIvSkxMZN++fUDqnRAXLVrE7t27c/SSb10V9rsaVn4eAJvj4Q9VCCGEELlJVVU6d+5Mly5dXFd6FS9ePNObET0sXY4V72VJPWcsvS2FEELohaIoDBw4kEOHDlGuXDm3rUeXLfYUe9KdR1LYhRBC5F1Hjx6lR48eJCWl1q127doxefLkHG+l30uXhX3f2TWAtNiFEELkbRs3buSbb75h165dubZOXRZ29c4Vxoq02IUQQuQxkZGRrsejR49m69attGvXLtfWr8/CfndMHWmxCyGEyEOWLVtGgwYN2LJlCwCFChWiYcOGuZpBl4XdbEwdhceo6LLvnxBCiHyqYcOGVKlShYCAAM0y6LKwK4qCgiLn2IUQQmgqJSWFadOm8ccffwBQrVo1Dh48SGhoqGaZdFnY7w5Q48Zh7oUQQogsffPNN8yaNYvp06e7pj3obVZzmq6PZWd0RzYhhBDCnaxWK4qiYDab6dChA7NmzaJLly5ax3LRZYvdeuc69jJFgjROIoQQoiD5/fffeeaZZ3j//feB1FPDr7766gPfM92ddFfY955dg8OZejvTWmWaaZxGCCFEQRIQEEBsbCzXr1/Ps6eDdXco/mrc76g4ARmgRgghhPudPHmShIQEnnjiCfz8/Ni/fz+FCz/cLblzg+4K+72ksAshhHCn69ev8+yzz1KsWDGOHDmCh4dHni7qoPfCLiPPCSGEcANVVVEUhWLFijF69GiCgoLw8PDQOla26LawW0xeeJp9tI4hhBAiH7Hb7XzwwQecPn2axYsXu+7Ipie66zwHqS11s9EDi8lT6yhCCCHyEYPBwO7duzl8+DBXr17VOs5D0V1hD/Qtj8Gg2wMNQggh8hi73c6PP/4IpBb2hQsXcuDAAUqWLKlxsoeju8L+VLUeGBQDSdbbJNvitY4jhBBC5/71r3/Rvn17fvvtNwBKly6Nv7+/xqkeni6bvk7ViVN14Myj1xAKIYTQj+7du+Pv76/pjVtyku5a7L/8ecQ1lKxBLncTQgjxgH755Rf69+9PcnIyAM8//zwff/wxRYoU0ThZztBdYT9xcReqemeAGv3FF0IIobFly5axdu1a1z3T8xtdHoq/SzrRCSGEyI6rV69SokQJAEaPHk2zZs149tlnNU7lHrpu8hoNRq0jCCGEyOO++uor6tWrxzfffAOAt7d3vi3qoOPCbjSY5VC8EEKILFWvXp2iRYtqHSPX6LIyKih4mr1lrHghhBDpOJ1OFi1aRFRUFAA1atTgxx9/zNet9HvpsrALIYQQmdm+fTsjRowgLCzMNc1isWgXKJfprrC/GDIMk9GCzZ6sdRQhhBB5hNPpxG63A9CmTRvGjh3LtGnTNE6lDd0VdqPBhM1hxea0ah1FCCFEHnDp0iWef/555syZA6Te0vutt94iMDBQ42Ta0N31YtduXwRU6TgnhBACSO3lHhkZSWBgoOt2qwWZ7gr7rjMrAFAUKexCCFFQXbhwgZiYGOrVq4e/vz87d+6kRIkSBb6ogw4LuxBCiIItNjaWZs2aUaRIEQ4cOEChQoV0eyc2d9BtYZd9MiGEKJj8/f0ZPHgwZcqUwcvLS+s4eY5uCztyKF4IIQoEVVVZvnw5x44dY968eQAMGTJE41R5l+6qo0ExoKDgYZK9NCGEKAhUVXXdtOXuoDMic/ptsQshhMi3VFXl7NmzVKtWDYPBwMcff4zJZKJ06dJaR8vzdNdif+KRDpiMFhxOu9ZRhBBCuEn//v1p0aIFkZGRAJQvX16KejbprsVe2Ks4NkcKqqpqHUUIIYSbtGrVihs3buDh4aF1FN3RVYu9a81f+T5ipdYxhBBC5LDLly/zzjvvkJKSAsCLL77IunXrKFOmjMbJ9EdXhd3LYiPFlqh1DCGEEDnso48+4pNPPmHt2rVA6rCwMtjMw9HdoXghhBD5Q1xcHH5+fgCMHDmSWrVq0a1bN41T6Z+uWuxCCCHyh2+//Za6devy3XffAeDj40P37t2llZ4DdFfYXV3m5LMXQgjdutvD/ebNmxonyX90dyjeoBjwMvuC7NUJIYRuqKrKV199xZNPPknp0qWpVasWP//8Mz4+PlpHy3d01WI/c60oVUvUx2AwYpAhZYUQQjd27NjB66+/zsiRI13TpKi7h66q4w9RJXms/DOoqirXsQshRB6nqipOpxOAFi1a8OabbzJhwgSNU+V/uirsADfiL5FovYXNkax1FCGEEJm4ceMGffr0Ye7cuUDq5Wvjx4+nYsWK2gYrAHRV2BuXv8zpy/u1jiGEECILBoOBI0eOsHv3blerXeQOXXWeqxIQy5XYG1rHEEIIkYHr168THR1NjRo1KFKkCNu2baNChQoYDLpqQ+qergq7EEKIvCkuLo4mTZrg7e3N3r17KVSoEJUqVdI6VoEkhV0IIcQ/5ufnx7/+9S/8/f3lxi0ac1thdzqdhIWFcfbsWSwWC5MnT6ZChQqu15csWcKWLVtQFIX+/fvTsmVLd0URQgjhBlu2bGH//v1Mnz4dgNGjR2ucSIAbO8/t2LEDq9XKmjVrGDp0qOuDh9RDNp999hmrV69myZIlTJ06NdvLNShGLKZCKIrRHbGFEEJkg9PpZO7cuSxfvpzff/9d6zjiHm5rsYeHh9OkSRMA6taty8mTJ12veXl5Ubp0aZKSkkhKSsr22MBxyR5UCvThdnKMWzILIYS4v6ioKMqWLYvBYGDBggXY7XY5l57HuK2wx8fHpxlVyGg0YrfbMZlSV1mqVCnatWuHw+GgX79+2VrmmhPlaVGiEjesW4HUnQeRs2Sb5g7Zzu4n2zjnffTRR2zfvp1FixZRqlQp1zjvsq3zFrcVdh8fHxISElzPnU6nq6jv3buX6Ohodu7cCcCrr75KcHAwderUue8yzRYzyX6/Y09OxsvsS0hIiLviF0jh4eGyTXOBbGf3k23sHq1bt+bChQtUq1aNW7duyTZ2o5SUlDRHuh+E286xBwcHs3fvXgB++ukngoKCXK8VLlwYT09PLBYLHh4e+Pr6EhcXl+UyK/nHcT3+Eqoqgx0IIYS73bp1iylTpmC1WgHo3Lkz3377LVWqVNE4mbgft7XYW7ZsyYEDB+jWrRuqqjJ16lSWLl1K+fLleeaZZzh48CBdunTBYDAQHBxMo0aNslxms0pR3EpMyHI+IYQQ/9y7777LvHnzKF68OH379kVRFIxG6bic17mtsBsMBiZOnJhmWuXKlV2PBw8ezODBgx9iyak3f8luhzshhBDZl5ycjKenJwDDhg2jZMmS9OnTR9tQ4oHIOH9CCCEAOHjwIPXr1+f7778HUgedGThwIGazWeNk4kFIYRdCCAGkdnqOjY3l3LlzWkcR/4DuhpQ1GkwYVDnHI4QQOeH777+nevXqlCxZkjp16nDixAkCAgK0jiX+Ad212C0mLzzMhbSOIYQQurdv3z46derE8OHDXdOkqOufrgr7ulNVaVtngNYxhBAiX2jUqBGvvPIKw4YN0zqKyEG6KuwJVjNnrhzEak/WOooQQuhOfHw8w4YNY968eUDq1UuzZ8/OcnAwoS+6KuweRge//nkUu9OqdRQhhNCdlJQUNm/ezMaNG3E4HFrHEW6iq85z3eucJcWe/ZvGCCFEQRcfH8+ff/5JlSpVCAgIYMOGDVSuXFkGmsnHdFXYU6ko+jrQIIQQmkhISKBZs2YYjUb27NmDl5cXNWrU0DqWcDMdFnYhhBDZ4e3tTdu2bTGbzRgM0iAqKKSwCyFEPnLo0CF27NjB2LFjAZg4caKcvixgdLcLp6CgKLqLLYQQbqeqKuPGjeODDz4gIiICkPtqFES6arE7nAYKeRTWOoYQQuQpMTExFC1aFEVRmDdvHrdu3aJatWpaxxIa0VXT94eoElQsJtdbCiHEXdOnTyc4OJiLFy8C8Oijj9KgQQONUwkt6aqwn4vxp7hveRxOu9ZRhBAiTyhfvjyBgYHExsZqHUXkEboq7B2qRfJ9xEpsjhStowghhCaSkpKYN28eNpsNgO7du7Nv3z5q166tcTKRV+jqHHuAV+pQsgbpPCeEKKBmz57N+++/j8FgYODAgSiKgqenp9axRB6iq8IufTuFEAWRw+FwjRQ3ePBgDAYDffr00TaUyLN02vSVEi+EKBh+/vlnGjduzN69ewEoXLgwo0ePplAhuX21yJhOC7sQQhQMdrudc+fOcfToUa2jCJ3Q1aF4IYQoCI4fP06ZMmUoXrw4wcHBhIeHU65cOa1jCZ3QVYs9MtaPjiFDMRstWkcRQgi3OHLkCK1atWLYsGGuaVLUxYPQVWHfFVmOwl6BMqSsECLfql+/Pp06deLVV1/VOorQKV0dijcoKlZ7MqqqyvjHQoh8wWq1Mnv2bAIDA+nbty8Gg4GPP/5Y61hCx3TV9G3xyEU+PxyG3WnVOooQQuSIW7dusXTpUhYvXozdLqNqin9OVy32CoXjtI4ghBD/mM1m4+rVq5QtW5bAwEBWr15N1apVMZl09ZUs8ij5LRJCiFyUnJxMmzZtSEpKYvfu3Xh6ehISEqJ1LJGPSGEXQohc5OnpSYMGDUhMTJRD78ItpLALIYSbnT59mu3btzNkyBAApk2bhsGgqy5OQkf0VdiVdA+EECJPU1WVt956i6NHj9KyZUtq164tRV24la4Ke4rdSKOg5/j54vdaRxFCiPtKSEjA29sbRVF4//33iYqKklurilyhq93Gz05Up3rpJzEYjFpHEUKITC1cuJDg4GAuXboEQI0aNWjVqpXGqURBoavCLoQQeuDl5YXBYODixYtaRxEFkK4Ke/3S0Ww9MR+H06Z1FCGEcHE4HKxcudLVy71nz5788MMPNGzYUONkoiDSVWGvVyqaa7eYSGKEAAAgAElEQVT/QFVVraMIIYTLu+++y+DBg1mwYAEAiqLg5+encSpRUOmq85z0hRdC5BX33rPi9ddf59q1a/To0UPjVELorMX+FynxQgjtnDt3jnbt2nHgwAEA/P39mTVrFgEBARonE0K3hV0IIbQTExPDkSNH2L59u9ZRhEhHV4fiXaTBLoTIZZGRkfj5+VGsWDEef/xx9u7dS40aNbSOJUQ6umqxqyqULVINRSq7ECIX/fzzzzRp0oThw4e7pklRF3mVrgr7pojKtKjZB6NBnwcahBD6VLNmTZ5++mnat2+vdRQhsqSrChmd6KV1BCFEAeB0Ovn000+xWCz07t0bo9HIypUrtY4lRLZk2WK3Wq0sWLCA4cOHEx8fz7x587BarbmRLZ3axa/zv6jdch27EMKtYmJimD59OrNnzyYlJUXrOEI8kCwL+8SJE0lKSuL06dMYjUb++OMPRo8enRvZ0mlc/grh57cDUtiFEDnL6XQSHR0NQLFixfjss8/YsWMHHh4eGicT4sFkWdhPnTrFkCFDMJlMeHl5MWPGDM6cOZMb2dJRlLsFXTrPCSFyjs1mo1OnTnTs2NHVQm/UqBElSpTQOJkQDy7Lc+yKomC1Wl0jLN28edP1OLelrlZX/f2EEDpgNpt55JFH8PLyIjExUVrpQteyLOy9evXi3//+N9euXWPKlCns2LGDgQMH5ka2dBTAaDRrtmMhhMg/Ll68yLZt2+jXrx8A06ZNw2yW7xehf1kW9hdeeIFatWrxww8/4HA4WLBgAdWqVcuNbOkpKhaT9IwXQvxz/fv359ChQ9SvX5+QkBAsFovWkYTIEVkW9kGDBjF37lyqVKnimta7d2+WL1/u1mAZunOKPSElFm8P/9xfvxBC12w2G2azGYAZM2bw888/ExwcrHEqIXJWpoX9P//5DxEREURHR/PMM8+4pjscDkqWLJkr4f5u/x+leabqNQAqFqujSQYhhD6tXbuWKVOm8N///pfSpUtTq1YtatWqpXUsIXJcpoV9xowZxMbGMmXKFMaMGfPXG0wmze5g9NOfgbQIuo63hz+PV2qrSQYhhD4lJCQQGxtLREQEpUuX1jqOEG6TaRdzHx8fypYty4IFC7h16xZXrlzh8uXL/P7772zcuDHLBTudTsaNG0fXrl3p2bMnFy5cSPP6nj176NKlC507dyYsLCxbg85UKXoLp+rIxo8lhCjoVFVl69at2O12APr06cORI0do3ry5xsmEcK8sz7G/8847HD9+nFu3bvHII48QERFBcHAwL7300n3ft2PHDqxWK2vWrOGnn35i+vTpLFiwAID4+HhmzZrFZ599RtGiRfnkk0+4efMmRYsWve8y2wadx+FQMZiMD/AjCiEKoo8++ohx48YxYcIEBg0ahKIocl26KBCyvCj86NGjbN26lWeffZZJkyaxdu3abA0pGx4eTpMmTQCoW7cuJ0+edL12/PhxgoKCmDFjBj169KBYsWJZFnUXuRRFCJENPXr0cA06I0RBkmWLvXjx4pjNZipXrszZs2dp164dCQkJWS44Pj4eHx8f13Oj0YjdbsdkMnHz5k1++OEHNm7cSKFChXj55ZepW7culSpVynK5DrsD1WElPDw8y3nFg5PtmjtkO+e869evM3fuXDp37kytWrX4/fff6d+/P1evXuXq1atax8uX5Pc4b8qysJcoUYKFCxcSGhrKrFmzAEhMTMxywT4+Pml2AJxOJyZT6ur8/f2pXbs2gYGBANSvX58zZ85kq7CbzWaMBhMhISFZziseTHh4uGzXXCDb2T0OHz7MoUOHqFy5MrVq1ZJt7Gbye+xeKSkpaY50P4gsD8VPmTKFsmXLUqdOHVq1asWWLVuYMGFClgsODg5m7969APz0008EBQW5XqtZsya//PILMTEx2O12Tpw4keY6+fuRUaGEEHddvXqVmzdvAtCwYUO2bdvG+++/r3EqIbR138KekJCAh4cH7dq1A6Bnz57MmTOH48ePZ7ngli1bYrFY6NatG9OmTWPkyJEsXbqUnTt3EhAQwNChQ3nttdfo0qULLVu2TFP470eRseKFEEBERARPPvkkw4cPd0174oknMBjkO0IUbJkeil+9ejWTJ0+mUKFCLF26lJo1a7Jt2zZmz56Nl5cX/fv3v++CDQYDEydOTDOtcuXKrsft2rVz7TBk18qfHuXfj0c+0HuEEPlT1apVqVOnDk888QSqqsrRPCHuyLSwL168mC+//JKoqCgWLVqEl5cX+/btY9CgQXTu3Dk3M7rEJHvKH68QBZSqqmzYsAGr1Uq3bt0wGo2sX79evhOE+JtMC7uXlxfVqlWjWrVqjBkzhtDQUL755ps0Pd1zm8Uog9MIUVDFxsYydOhQLBYLzz//PF5eXlLUhchApoXdaPxrEJjChQszc+ZM180TtNL7sQjkfuxCFCxxcXH4+flRpEgRFi9eTKVKlfDykrs8CpGZTAv7vXvChQoV0ryog+vmbkKIAsBut9OvXz9+/fVXduzYgcViSXNDKiFExjIt7OfPn6dXr17pHt/12WefuTdZBlTksJsQBYXJZMLHx4dChQpx8+ZNGQ5WiGzKtLAvXLgwN3Nki7TYhcjfYmJi+O9//8vLL78MwPTp07FYLGlODQoh7i/Twt6gQYPczJEt2bgBnBBCx1599VX27NlDlSpVeOKJJ+RcuhAPIcshZfMSqetC5D9Op9M1qExYWBh79uyhfv36GqcSQr901cX80MVSWkcQQuSgb7/9lsaNG7tu0vLYY48xePBgOfQuxD+QrcIeFRXF7t27cTgcXLx40d2ZMnX2ehHN1i2EyHkXLlwgMjKSo0ePah1FiHwjy8K+bds2BgwYwOTJk4mNjaVbt25s2rQpN7IJIfKhAwcO4HCkDjb16quvcvDgQdq3b69xKiHyjywL+yeffMIXX3yBj48PAQEBbNiwgUWLFuVGtnTaVLmgyXqFEDlj2bJldOjQgY8//hhIvafEI488onEqIfKXLAu7wWBIM4xs8eLFNbt7UkChJE3WK4TIGR06dOCZZ57h6aef1jqKEPlWlhW6atWqrFy5ErvdzpkzZxg7dizVqlXLjWzpyAA1QujLrVu3GDx4sOscekBAAOvWraNGjRoaJxMi/8qysI8bN46rV6/i4eHBqFGj8PHxYfz48bmRLR2nXO8mhK6cPn2alStXsmDBAq2jCFFgZHkd+9q1a+nduzdDhw7NjTz3parSYhcir4uLiwPAz8+P0NBQ1q5dS7NmzTROJUTBkWWL/erVq3Tp0oVXX32VTZs2kZSk3XnuZLtc2ypEXvb777/TqFEj3nnnHde0Fi1a5ImbSAlRUGRZ2N955x127drFgAEDOHHiBC+88AJvv/12bmRL57cYf03WK4TInnLlylG6dGnKly+PKmNAC6GJbA0pq6oqNpsNm82GoihYLBZ358rQyegAWleL1mTdQoiM7dmzhxs3bvDiiy9iMpnYunUrJpOuRqsWIl/J8q9v0qRJ7Nixg+rVq/Pcc88xZswYPDw8ciObECKPi4uLo0+fPiiKQqtWrfDx8ZGiLoTGsvwLrFixIhs2bKBo0aK5kef+WfzjtI4ghACSk5Px9PTEz8+PefPmUbp06TTjXQghtJNpYV+zZg1du3bl1q1bfP755+lef+ONN9waLCMlfRJzfZ1CiL84nU5GjBjBsWPH+OabbzCbzbRr107rWEKIe2TaeU46vggh/s5gMJCQkEBiYqLrjmxCiLwl0xZ7t27dAChTpgwdO3ZM89qqVavcm0oIkWckJCSwc+dOnnvuOQCmT5+O2WzG09NT42RCiIxkWtiXLVtGfHw8q1ev5tKlS67pDoeDzZs38/LLL+dKQCGEtvr27cv27dvZunUroaGh+Pr6ah1JCHEfmRb2ChUqcOrUqXTTLRYL06dPd2uozMjJASFy37Bhw6hatSp169bVOooQIhsyLexPP/00Tz/9NG3atKFy5coAxMfHc+XKFapWrZprAe9lc8jIc0K42w8//MD48eNZsWIFgYGBBAcHExwcrHUsIUQ2ZTny3I8//sjIkSOJiYmhbdu2DB48mPfffz83sqXPciVQk/UKUZAcP36co0ePsmvXLq2jCCEeQpaF/YsvvuCdd95hy5YtPPPMM2zevJl9+/blRjYhRC45efIkTqcTgNdff51du3bRtWtXjVMJIR5GloUdwN/fnz179vDUU09hMplISUlxd64MFfHUZr1C5Gfr1q3jqaee4pNPPgFSL2l77LHHNE4lhHhYWRb2KlWq0K9fP6KioggNDeXNN9+kdu3auZEtnaoBsZqsV4j8rFmzZtSrV486depoHUUIkQOyHFJ26tSpHD9+nKCgICwWC88//zxNmzbNjWxCCDdISUlhxowZtG/fnuDgYIoXL863336LoihaRxNC5IAsW+w2m43vv/+ef//73zz//PMcPnwYq9WaG9mEEG5w/Phx5syZw8yZM13TpKgLkX9kWdgnTpxIcnIyU6dOZcaMGdjtdsaPH58b2YQQOSQlJYX4+HgAGjZsyJIlS1i8eLHGqYQQ7pDlofhTp07x9ddfu56PGzeOtm3bujWUECLnREVF0aVLF4KDg5k3bx4AL7zwgsaphBDukmWLXVVV4uL+ul1qXFwcRqM2A8XIyHNCPLgSJUrg4eGBh4cHDodD6zhCCDfLssXep08fXnrpJZo3bw7Arl27eP31190eLCMnrwbQrHKMJusWQk9+/vlnLl26RJs2bTCbzWzbtg0vLy+tYwkhckGWhb1Tp07Url2bo0eP4nQ6mTt3Lo8++mhuZEsnyZ5lXCEKvISEBDp27IjT6eTEiRP4+flJUReiAMm0UjqdTlatWsX58+cJCQnJE3dzk467QmTO4XBgNBrx9vZm1qxZFClSBD8/P61jCSFyWabn2MPCwti+fTteXl58/PHHrk43WgopFa11BCHyHFVVmTlzJu3atcNutwPw4osv8vTTT2ucTAihhUwL+9GjR1m5ciXDhg1j+fLlfPvtt7mZSwiRTYqiEBkZSVRUFH/88YfWcYQQGsv0ULyHh4dr0IoiRYrIABZC5CE2m419+/a5OrXOmDEDgMKFC2sZSwiRB2TaYv97ITcYsnW/GCFELhg4cCCdO3fm0KFDQGpBl6IuhID7tNgvX77MyJEjM30+bdo09yYTQmSqb9++WCwWqlevrnUUIUQek2lhHzFiRJrnDRo0cHsYIUTGIiIiGD9+PPPnzycgIIAGDRrI36QQIkOZFvaOHTvmZo5sibrlA9zQOoYQuW7nzp189913bNq0iVdeeUXrOEKIPExXI778mVBI6whC5Jrz589Tvnx5DAYD/fv3p06dOjRp0kTrWEKIPE5XPeIalbtMQkqs1jGEcLtt27YRGhrK0qVLATAajVLUhRDZkq3CnpiYSEREBKqqkpiY6O5MmaoRmDpOfMVidTTLIERuCA4OpkKFCpQuXVrrKEIIncmysB86dIjnn3+egQMHcu3aNZo3b87+/ftzI1s6igLeHv48XkluGyvyF4fDwUcffcSJEycAKFmyJAcPHqRNmzYaJxNC6E2Whf29997j888/x8/Pj+LFi7Ny5UpmzpyZG9mEKDDCw8MZO3YsYWFhrmkydoQQ4mFk2XnO6XQSGBjoel6lShW3BhKioHA6naSkpODl5UWDBg344IMPpIUuhPjHsmwSlCxZku+//x5FUYiLi2PBggXZOu/ndDoZN24cXbt2pWfPnly4cCHDeV577TW++OKLh0svhE5FR0fTvn37NIM+9ezZk2LFimmYSgiRH2RZ2CdOnMjmzZu5cuUKLVq04MyZM0ycODHLBe/YsQOr1cqaNWsYOnQo06dPTzfPnDlziIuLe7jkQuiYv78/t2/fJjY21nVHNiGEyAlZHooPCAjgvffee+AFh4eHuy7PqVu3LidPnkzz+vbt21EU5YEu4THJOUehY+fPnycyMpLChQtjsVjYsmULfn5+coMlIUSOyrKwN2/ePMMvnp07d973ffHx8fj4+LieG41G7HY7JpOJX375hS1btvDhhx/y0UcfZTusl8mE1ZpEeHh4tt8jHoxsW/ewWq3861//wm63s2zZMtnOuUC2sfvJNs6bsizsK1ascD222+189913WK3WLBfs4+NDQkKC67nT6cRkSl3dxo0buXr1Kr179+bSpUuYzWbKlClD06ZN77tMs9mMgoOQkJAs1y8eXHh4uGzbHKaqqmvHePLkyZjNZnx9fWU7u5n8LrufbGP3SklJSXekO7uyLOxlypRJ8/y1117jxRdfZODAgfd9X3BwMN9//z1t27blp59+IigoyPXa8OHDXY/nzp1LsWLFsizqAA6nQ2dj5YmCSlVVli5dyubNm/nyyy8xGo10794dkFaOEMK9sizsR48edT1WVZVff/2VlJSULBfcsmVLDhw4QLdu3VBVlalTp7J06VLKly/PM88881BhnU47ihR2oQOKonDo0CFOnDjBL7/8IrdXFULkmiwL+4cffuh6rCgKRYoUybCH+98ZDIZ0vecrV66cbr5BgwZlJ6cQeZ6qqhw7dozHH38cgBkzZpCSkkKpUqU0TiaEKEiyLOxt2rShR48euZFFCF0bMmQIn332Gdu2beOJJ56gaNGiWkcSQhRAWR7Y/vzzz3MjhxC617VrV1q1akW5cuW0jiKEKMCybLGXLFmSXr168dhjj+Hh4eGa/sYbb7g1mBB5XVRUFJMmTWL69OkUKVKEhg0b0rBhQ61jCSEKuCwLe926dXMjhxC6s2HDBtatW0fNmjUZPHiw1nGEEAK4T2HfsGEDHTt2zFMtc7PJAzvJWscQBVh0dDSBgYEoisKAAQOoUKECHTp00DqWEEK4ZHqO/bPPPsvNHELkebt376ZBgwYsX74cAJPJxHPPPSdDwgoh8hRdXRWuqk6tI4gCLCgoCF9f3zR9TYQQIq/J9FD8r7/+muFAMneHyMxqrHh3sDvtKMZcX60ooFRVZe3atdSsWZNatWpRunRpwsPDsVgsWkcTQohMZVrYK1SowKJFi3IzixB5yvHjxxkwYAChoaFs3boVQIq6ECLPy7Sw370xixAFiaqq2O12zGYzwcHBTJ06lbZt22odSwghsi3Tc+zBwcG5mUMIzcXGxtKrVy9Gjhzpmta/f3/Kly+vYSohhHgwmbbYx40bl5s5hNCcl5cXkZGR3Lp1C6vVKofdhRC6lOUANULkZ9HR0fz22288+eSTeHh4sH79egIDAzEYdHXBiBBCuOiqsBsNJpxYtY4h8gmr1UqLFi1ISEjg8OHDBAYGUqJECa1jCSHEP6Krwm5QDMiV7CKnWCwWhgwZQnJyMgEBAVrHEUKIHKGrwi7EP7Vp0ya++uorli5ditFopE+fPlpHEkKIHKWrE4k2hxyGF//Mxo0b2bFjBydPntQ6ihBCuIWuCrsQDyMiIsL1eNasWezZs4fHHntMw0RCCOE+UthFvhYWFkbjxo05duwYAMWKFaNq1aoapxJCCPeRwi7ytZYtWxISEkLhwoW1jiKEELlCCrvIV27evMmIESOIjY0FoFGjRmzfvl1a6UKIAkMKu8hXVq1axaJFi1i4cKFrmtwvXQhRkOjqcjeDYkSVK9nF38TFxeHr64uiKPTv3x8/Pz969OihdSwhhNCErlrsRoPcjF2kdeTIEUJDQ1m5ciUAJpOJXr16YTLpap9VCCFyjK4KuxB/V7p0aWw2G7dv39Y6ihBC5Am6atY4nHbZFRHs2LGDMmXKUL16dcqWLcuPP/6Ij4+P1rGEECJP0FWZdKpyfr2gO3nyJF26dOHNN99EVVUAKepCCHEPXbXYRcHldDoxGAzUqlWLUaNG0bp1a+ntLoQQGZDCLvK0+Ph4xowZg6enJ9OnTwdg2LBhGqcSQoi8Swq7yNNMJhOHDx/Gw8OD5ORkPD09tY4khBB5mhR2kefcvn2bX3/9leDgYDw9PVm3bh0lSpTAYrFoHU0IIfI8XRV2BTmnmt/Z7XZatmzJjRs3OHjwIIGBgZQrV07rWEIIoRu6Kuwmoxk7Dq1jCDcymUy88sorXLt2DT8/P63jCCGE7uiqsIv8af/+/Xz++efMmzcPg8HA66+/rnUkIYTQLX1dx+6U69jzo0WLFrF27VrXPdOFEEI8PF0Vdocqh+Hzi6ioKNfj2bNn880339CgQQMNEwkhRP6gq8Iunefyhzlz5hASEsLx48cBKF68OCEhIRqnEkKI/EFfhV1GGssXgoODqVixonyeQgjhBvoq7NJi16XExESmT59OXFwcAE2bNuXAgQPUrVtX42RCCJH/6KuwS13XpeXLlzNz5kzmzJnjmib3SxdCCPfQ2berVHa9SElJwWKxoCgKr732Gjabjddee03rWEIIke/prMUuhV0PTp06RdOmTVm9ejUAZrOZwYMHU6hQIY2TCSFE/qerwi70wc/Pjz///JPff/9d6yhCCFHg6OxQvMirwsPD8fX1JSgoiHLlyhEeHk6xYsW0jiWEEAWOtNjFP/bLL7/w7LPP8p///AdVVQGkqAshhEakxS7+saCgIAYNGsQzzzwj/SCEEEJjUtjFA7NarcyaNQur1cqECRMAGD9+vMaphBBCgByKFw/B4XCwadMmNm/eTEJCgtZxhBBC3ENXhT3RGqd1hALLarVy5swZALy8vFi1ahV79uzB29tb42RCCCHupbtD8RWL1dE6QoHjdDpp164df/zxBwcPHiQgIICqVatqHUsIIUQGdFXYC1n8eLxSW61jFDgGg4HnnnuOc+fOYTabtY4j8hC73Y7T6dQ6RoasVqvWEfI92cb/nMFgyPEhtnVV2EXuOXnyJCtWrGDatGkYDAbeeOMN6fEu0rh9+zZGozFPjvtfuXJlrSPke7KNc4bVaiUpKQlfX98cW6bb/iKdTidhYWGcPXsWi8XC5MmTqVChguv1ZcuWsXXrVgCaNWvGG2+84a4o4iHMnDmTLVu20K5dO5o2bSpFXaRht9sxGo15dphgm82GxWLROka+Jts4Z1gsFhITE7Hb7Tm2k+y2znM7duzAarWyZs0ahg4dyvTp012vXbx4ka+//prVq1ezdu1a9u/fT0REhLuiiGy6e1tVSC3sa9eupWnTphomEnmV0+nMky11IfTIaDTm6CkttxX28PBwmjRpAkDdunU5efKk67WSJUuyePFijEYjiqJgt9vx8PBwVxSRDUuWLOHll1/m559/BlI/oxYtWmicSggh8r+cPiLqtl3u+Ph4fHx8XM+NRqPrUIPZbKZo0aKoqsrMmTOpUaMGlSpVynKZNpuN8PBwd0Uu0Gw2Gz4+Phw7dgybzaZ1nHwvP/weV65cOU//rsgYC+4n2zhn2Gw2zp07l2PLc1th9/HxSfOh//3QXUpKCqNGjcLb2zvbo5aZzWZCQkJyPGtBZLfbWbx4MT169MDPz4+QkBBq167Nk08+qXW0fC88PFz3v8d3e0NreY71hx9+4P/+7/+oUqUKkFpkypYty+zZs7HZbKSkpDBjxgwuX76Mw+GgVKlSjBgxgsDAQACOHTvGRx99hN1uJzExkRdffJGXX345W+teuXIlq1atYtCgQbRtm/mVOuvXrycyMpJhw4b98x/4juTkZN5++21u3LiBt7c3M2bMoGjRovd9T1hYGD/99BMbN250TevZsydhYWGuTnApKSm0adOGXbt2AbBmzRq+/vprDAYDNpuNt956iyeeeML1/oSEhGyNYzFv3jx2796NyWRi1KhR1KmT9pLljRs38umnn+Lr60vHjh3p3LkzsbGxvP3228THx+Pv78/kyZMJCAjI9jbSG6vVSu3atdP8PaWkpKQ50v0g3FbYg4OD+f7772nbti0//fQTQUFBrtdUVWXgwIE88cQTvP766+6KIO5j2bJljBo1ikuXLjFp0iQAOR0iHtrwzeF8eeJCji7zpccqMLPD/XeAGjZsyPvvv+96PnToUHbt2kXjxo154403eOWVV1ynlA4ePEi/fv1Yt24dly9fZvLkySxevJhixYqRnJxMr169KFeuXLb6lXz77bfMmTOHRx999J/9kA/hiy++cN2fYevWrcyfP58xY8ZkOn9SUhLh4eEEBQXxww8/pCnOmdm6dSsHDhxg2bJlmM1mLl68yL/+9S82bNiQ5U7EvU6dOsWRI0dYt24dV65cYdCgQXz11Veu12NiYvjwww9Zv349fn5+9OnTh9DQUFatWkVISAj9+/fn4MGDvPfee0yZMiXb6y3o3FbYW7ZsyYEDB+jWrRuqqjJ16lSWLl1K+fLlcTqdHDlyBKvVyr59+wAYMmQI9erVc1ccQepQsAaDAUVR6NWrF1euXJGrEUS+YbVaiY6OpnDhwpw5cwZfX980/USefPJJypcvz9GjRzl27BgvvPCC6y6Enp6efPrpp+l6+UdFRTFq1CgcDgeKojBmzBhOnDjB6dOnGT16NO+//z7lypUDUlvSI0eO5PLly9hsNsaOHZtmWe+++y4nT54kNjaWatWqMW3aNMLDw5kxYwYmkwkvLy8++OADrl27xsiRIzGZTDidTt59911KlSrlWk54eDivvfYaAE2bNmX+/Pn33S7//e9/CQ0NpWnTpqxatSpbhX316tWMHDnSNW5FuXLl2LhxI0WKFEkzX79+/UhMTHQ9r1y5MmFhYWmyNm7cGEVRKF26NA6Hg5iYGNfOQVRUFI8++ij+/v4A1K5dmxMnTvDbb7/x1ltvAamNxIkTJ2aZWfzFbYXdYDCk+zDuve7xf//7n7tWLTIQGRlJv379eP311+ncuTMWiyXdF48QD2tmh5AsW9fucPjwYXr27MmNGzcwGAx06dKF0NBQNmzY4Cq49ypXrhyXL18mOjqaatWqpXkto+uIZ86cSa9evWjRogVnzpxh1KhRrF+/ni1bthAWFpZmHatXr6ZMmTK8//77nD9/nt27d+Pn5wek9jny8/Nj6dKlrpEcr169yuFAzZcAACAASURBVI4dO2jTpg29e/dm165dxMXFcfDgQerUqcPbb7/NsWPHuH37dprCHh8f78rq7e3N7du377uN1q1bx8SJE11F9+rVq5QoUeK+74mOjk63/f5e1AEWLlx43+XcPZR+1928dwt7hQoV+O2337h+/Tre3t4cOnSIihUrUr16dXbt2kWNGjXYtWsXycnJ912PSEtXY8WLh2cwGIiIiODo0aNaRxEixzRs2JAVK1awatUqzGYzZcuWBaB48eJcunQp3fwXLlygVKlSlC5dmj///DPNaxEREZw+fTrNtHPnzvH4448DUL169XTvuVdkZCR169YFoGLFivTp08f1moeHBzExMQwZMoRx48aRmJiIzWajf//+REdH07t3b7Zv347JZOKll17Cz8+P1157jVWrVmE0GtOs597+SwkJCa6dh4ycO3eOX3/9lenTp9O3b18UReGLL75wZbq382NCQgKenp4AlClThitXrqRZ1r59+4iOjk4zrV+/fvTs2dP1797W+t+z3l3HvTtQhQsXZuTIkQwaNIghQ4ZQs2ZNihQpwuuvv86lS5d4+eWXiYqKomTJkpn+jCI9Kez52G+//UZkZCSQ+kVz8OBBZs6cqXEqIXJekSJFmDVrFmPGjCE6OprHHnuM69evuzqCAezdu5cLFy7QoEED2rdvz7p164iJiQFSC864ceO4du1amuVWrlyZY8eOAXDmzBnXofuMVK5c2XUk8uLFiwwdOjTNuq9cucJ7773HkCFDSE5ORlVVvv76azp27MiKFSuoWrUqa9euZefOnYSEhLB8+XJat27N4sWL06wnODiYPXv2uJZ7v46Y69at46233uLTTz/l008/Zfny5Xz11Vf/396dR1VV9X8cf18mmRESTU0cQE1RU3LEMcVEoEiQQQTTcHpMDYfCEHFCcvZxHhInnADFAcdQeCS1VKRQKydytgQTTESZ7vn9weL8uIGABgLX/VqrtfDce8/ZZ0vuu/c55/shOzsba2trjh49qtLG1q1bA+Dq6sqqVavIzc0F4MaNGwQGBhb5krF27VrCwsLk//45sNvY2HDy5EmUSiX3799HqVSqXKPPzc3l119/Zfv27SxdupTff/8dGxsbEhIScHNzY9u2bTRs2BAbG5sXnqNQlKgwoaZu3rxJjx49aN26NYcPH0ZDQ6PYpUlBUBdWVlb4+PgQHBzMN998w5o1awgJCZGXi99++23WrVuHpqYm77zzDl9++SVjx45FU1OTp0+fMnDgQHr27Kmyz6+++opp06axYcMGcnNzS7yBy9PTk4CAALy9vcnLyyMgIIBr164B0KZNG1atWsXgwYNRKBQ0aNCAlJQU2rRpQ2BgIHp6evLlS0mS8Pf3Z/Xq1SiVSr7++muV4wwaNAh/f38GDRqEtrY2ixYtAmDOnDm4uLjQokULIP+egwMHDrB//375s/Xq1ePdd9/l6NGjjBgxgqCgIAYMGECNGjWoWbOmfCOto6MjqampeHl5oa2tTV5eHgsWLHjpO9NbtWpF+/bt8fDwQKlUEhQUBEB0dDSZmZl4eHgAyG0YNmwYZmZmNG7cGH9/fyB/9SUkJOSljvumU0iSJFV2I0pTcNv/beX3DOjgV9nNqTa++uorunbtirOzc5nerw6PYVUH6tDPVeFxt5KU9VEsdRIWFkaPHj1USndXpDexjytKcf8/FYx7rVq1euknlsSMXU0olUrWrl1LWloaAQEBAGLZXRDeIH369KFevXqV3QyhChDX2NXE8+fPWb9+PZs3b1ap+S4IwptBDOpCATFjr8aUSiV37tyhYcOG6Ovrs2XLFszNzUu8S1YQBEFQb2LGXk1JkoS7uzv29vakpaUBYG1tTe3atSu5ZYIgCEJlEjP2akqhUNCtWzf09PTIy8ur7OYIgiAIVYQY2KuR27dvs3nzZgIDA1EoFIwfPx6FQlHukX+CIAhC9SWW4quRoKAglixZwrFjxwDkuu+C8CY6c+YMXbp0kaueubi4MH78ePnRoUePHuHv74+Pjw9eXl5MmjRJpQBNQkICw4YNw8fHB1dXV7Zt21bmY2/dupX+/ftz6NChEt8XFRXFwoULX+0ESxETE6NSBKck3377Ld26dSMrK0veNmXKFOLj41Xe17VrV/nnY8eOyX3r5ubGkSNHXqmdERERuLi44O7uTlxcXJHXT548ySeffMKgQYPkuvfZ2dlMmjQJd3d3PvvsM27evPlKx35TiRl7FVf4WdGQkBAcHBxUgi0EoSo4d+MQNx9eKNd9NqrVhg6NXxyJCm9muhtAcHAwJ0+elIvRlGb//v04ODhw8OBBXFxcSn1/YmIimzZtYu3atRgYGJCWloaHhwdWVlZyTG5ZpKamEhYWxu7du8nKysLLy4uuXbvKz2srlUoCAwMJCwujQYMGTJ48mYSEBC5fvoy+vj4RERH8/vvvzJ49m9DQ0DIf900nBvYqbPfu3fj7+7Nv3z6sra2pV68e7u7uld0sQaiS3pR0N8gv1WpnZ0d4eHip/XLmzBksLCzw9PTkyy+/LNPAHhkZyaeffipPKkxNTYmMjCzyxM3UqVO5ffu2/GcTExNWrFgh//nChQu0a9cOHR0ddHR0sLCw4PLly3Ime1paGsbGxnIf2tjYkJiYyP379+UvWE2aNCE5ObnUNgv/TwzsVZixsTF5eXncuXMHa2vrym6OILxQh8YOpc6uK8KbmO4G4ODgwJkzZ8rUR5GRkbi5udGkSRN0dHRISkrivffeK/a9BZf2ikt3MzExKfL+0jLSCyfRQX66W0ZGhvxnMzMznj9/TnJyMo0aNSI+Pp53332XFi1aEBcXh52dHUlJSTx48IC8vLwiteqF4olr7FWIJElERETIv/h9+/bl559/xt7evpJbJghV05uY7vYyHj9+THx8PFu2bMHX15eMjAy2bt0qt6ngfoQCBaEv9erVK5Ludv78eW7duqWyberUqSrpbmPHjlV5vbR0N4VCwfz585kxYwYjR46kcePGmJqa4urqiqGhIV5eXsTExGBtbS0G9ZcgBvYqZNu2bYwePVol8KC4b8mCIKh6k9LdXsb+/ftxdXVlw4YNhIaGEhERwalTp3j06BHW1tbExMTI701ISJCvn7u4uBAaGkpmZiYAf/31FwEBATx79kxl/3PmzFFJdyu8DA/54Tfnz58nKyuLJ0+ekJycTLNmzVTec/LkSUJDQ1m/fj23b9/G1taWixcv0qVLF3bs2IG9vb0IsHpJYim+khVk8CgUCtzc3EhKSmLMmDGV3CpBqH7elHS3F1m3bh3vvvuuys1/kZGRKpkRenp6fPjhh0RERODr68tvv/2Gs7MzBgYGaGtrM2vWLADatWsn35GupaXF8+fPmThxYpHLF6UxNzeXn0qQJIkJEyZQo0YNfvjhB86fP8/YsWOpXbs2bm5u6Orq8tFHH9G0aVMePXrE0qVLWbNmDUZGRqUu+QuqRLpbJfrjjz/w8/PDw8OjTDe0VDR1SB2rDtShn0W6W9Vz/Phx9PX16dKly2s53pvYxxVFpLupkWfPnnHy5EmMjIyqxMAuCEL11aJFCxEEIwBiYH/t/vjjD3Jzc2nQoAFNmjQhJiamzM+iCoIgvIgY1IUC1ermuXdMX+76TlVz9+5dunbtyqhRo1AqlQC0bNlSVI8TBEEQyk21mrG3afBBZTfhX6lfvz79+/ev9tdXBUEQhKqrWg3s1Y0kSURFRXHnzh38/PxQKBSsXLmyspslCIIgqLFqtRRf3Tx//pzg4GCWLFkiZ6YLgvD6/LNgirqqzECc12X06NGMGjVKZVvv3r1Vgm2Sk5Px8fEB8uvQr1mzBi8vL7lfrly58srHT0pKkvf9T7Gxsbi6uuLh4UFERASQ/+//uHHj8PLyYsSIEXLNhNdBzNjLmSRJpKSkUKdOHfT09AgNDcXU1BRTU9PKbpogvHH+WTBFnb0oEKdfv34VGojzOty/f5/MzExyc3O5c+dOmQrWrF+/nrS0NLZu3YqGhgYXLlxgzJgxHDlyBG1t7Zc6/rfffsv+/fvR09Mr8lpOTg7ffPMNu3btQk9Pj0GDBtG7d2+io6Np1qwZ48aN4+DBg6xatYrAwMCXOu6rEgN7OZIkiVGjRnHq1ClOnz6NiYkJNjY2ld0sQXgtIs/NLXZ7q/o9aVEv/9nq+CvhPPj7RpH3mBtZ0OtdLwCu/nmWpDuxuHWYUuLxoqKiiIuL4/nz56SmpjJkyBCOHz/OtWvX+OKLL3B0dKRr166cOnWKpKQkQkJCUCqV1KlTh4ULFzJixAjMzMx4/Pgx69atIyAggLt375KXl8ewYcNwcFCtfZ+RkcHUqVN58uQJKSkpeHl5YW9vz+DBgzl06BAKhYJZs2bRpUsXLCwsCA4OBqBmzZqEhITw66+/snDhQrS1tXF3d0dXV5dt27aRm5uLQqFgxYoVmJqaMnPmTC5dukStWrW4d+8eq1evRlNTk2nTppGVlUWNGjWYPXt2kfrxhRUOxLl06VK5BOLcvHmTwMBAcnJy0NXVZc6cOcyePRsHBwd69OhBfHw8hw4dYu7cuXzwwQc0adIES0tL4uLi2LdvH/r6+oSGhqKpqUm/fv1e6nx2795Nnz590NXVZfv27fj7+5f4uwEQHh5OVFQUGhr5C9Nt2rRh165dKoP606dPGT16tMrnOnXqVGSlx8LCguXLl/PVV18VOU5ycjIWFhZyldD333+fc+fOcf78eYYPHw5Ajx495Eja10EM7OVIoVDQtGlT7t69S0ZGhigHKwgV7OnTp2zYsIGDBw+yadMmIiIiOHPmDBs3bsTR0VF+X1BQEIsXL8bS0pLIyEg5LczJyYm+ffuydetWzMzMWLhwIRkZGbi4uNC5c2fMzMzkfdy6dQtHR0c+/PBDHjx4IC9rN2/enISEBN577z3OnDlDQEAAXl5ehISEYGVlRWRkJOvXr8fW1pasrCwiIyMBWLNmDevWrUNPT4+goCBOnjyJvr4+6enp7Nq1i0ePHvHhhx8CMG/ePHx8fOjZsyc//PADCxcuZNGiRSp98aJAnEOHDpVLIM68efMYOXIkPXr04Pjx41y+fPmFfy9//PEHUVFRmJqaoq2tzXfffccnn3zCgQMH2LBhAzNnziz1fAoolUoOHDhAeHg4WlpaODo68sUXX6Crq/vC40P+Uvg//w3+58qpgYEBYWFhJe4HoF+/fty9e7fY114UdFN4u4GBAU+ePCn1OOVFDOz/0sOHD9m2bRvjx49HoVAwYcIEJk2aJH9LFIQ3RWkzbIAezT1KfU+ztzvS7O2OZTpmQQ0IIyMjLC0tUSgUmJiYqFx3hfz/Ty0tLfPb6eYmb2/cuDGQP+uytbUF8oNLLC0tuX79OsuXLwfyZ7guLi5s3ryZ7777DkNDQzkwxd3dnT179pCamkrv3r3R0tIiOTmZmTNnAvlLtY0aNVI5HsBbb72Fv78/BgYGcoBM4SAZMzMzmjRpAsDVq1dZu3Yt69evR5IktLSK/tNdsBSflpbGZ599Jgfi1KlT54WBOLa2tqSkpBQbiKNUKmnZsqW87caNG7Rr1w6APn368PTpU44dOya/XriIaeHLj25ubsyYMYMmTZrIIS9lOZ8C33//PU+fPpVr7yuVSqKjo3Fzc5ODbAoqs2VmZsoDvrGxMRkZGRgaGsr7iomJoUuXLvK2ss7YS/KioJvC258+fVok8rYiiYH9X/r666/ZvXs3VlZWODo6lvgLKghC+SprDYjatWtz8+ZNGjVqxLp16+QBtuDzBWEvffv2JSMjg6tXr2Jpaakym/vmm29o27YtXl5e/Pjjj5w4cQKALl26sGDBAh48eMD06dOB/AF83rx51KtXj/Pnz8s3qhV84X/y5AnLli3jf//7HwDDhg1DkiSaNm3Kvn37gPxktps3bwL5meSfffYZNjY2JCcnc+7cuReea0EgzpAhQ9i7dy82NjZyIE7v3r0B1UCcBg0a8Pnnn+Pg4ICZmZkciPP555+r7Lcg5MbW1pb9+/eTmpqKjo6OfG6Fk/EKT2waNWqEJEmsX7+eQYMGvfT57Nq1i+DgYHr16gXkl2QODg7Gzc2Nli1bcvToUQYOHCifV+vWrQEYMGAAK1aswN/fH4VCQWJiIt988w1HjhyR913WGXtJLC0tuXXrFunp6ejr65OQkICvry/379/nxIkTtGnThvj4+Nf6mLMYhV5BTk6OfJ1m+vTp2NjYiGhVQajCZs6cSUBAABoaGpibmzN06FC2bNkiv+7u7s60adMYNGgQWVlZjB07lrfeektlHx988AHBwcEcOnQIIyMjNDU1yc7ORkdHh379+nH69GksLCwAmDFjBv7+/vL18zlz5pCSkiLvy9DQEBsbGzw8PNDS0sLY2JiUlBRcXFyIj4/H09OTWrVqoauri7a2Nv7+/syYMYOsrCyeP3/O1KlTSzzfwoE4y5YtK7dAnKCgIFavXo2uri4zZ86UU9+io6PlVYniDBw4kGXLltG5c2eAF57PhAkTCAgIwNzcHMhfaUlKSlK5KfD9998nKyuLxMREOaRnx44daGlp0aBBA3mlxNfXl6VLl8p9rKWlxerVq8st3yA6OprMzEw8PDyYMmUKvr6+SJKEq6srderUYdCgQfj7+zNo0CC0tbVfeKmhIlSrEJhXKYZf3mJiYpg0aRLh4eFqVwpWHcJJqgN16GcRAlMxkpOTuXz5Mo6OjqSlpeHk5ERcXFyV7OeK6OPFixczevToIjfuqTsRAlPJ8vLySE1N5dKlS2o3sAuCULnq1q3LwoUL2bx5M3l5eUyePLlKDuoVxdPT840b1CuCGNjLICYmBltbWwwMDLC3tycxMbHERzMEQRBehb6+PqtXr67sZlQaEWRTPsSt26XYvXs3Hh4ezJkzR94mBnVBEAShqhIDeykcHR0ZOHAgQ4YMqeymCIIgCEKpxMD+D+np6Xz++efyIye6urqsW7euSAEHQRAEQaiKxMD+Dw8fPmTPnj1s3bq1spsiCIIgCC9N3DxHfiGIp0+fUq9ePaysrNi/fz/vvfdeZTdLEAThlfTu3Zu6deuioaFBXl4emZmZzJ49m9atWyNJEtu3b+fAgQNyQa3hw4fLz60/fvyYefPmcfv2bXJzc6lbty6zZs0qtsxsZTl06BABAQEcPXqUOnXqALB8+XJq1aolF8GB/PoEixcv5p133iEhIYGVK1eSm5tLZmYmLi4uDB48+KWPHRsby8qVK9HS0sLV1RV3d3eV13/55RemT5+Ojo4OLVq0YOrUqWhoaBAcHExiYiIGBgZMnjy5QseYN35gf/DgAX369KFJkybs3bsXDQ0N2rdvX9nNEgRB+Fc2bNggP//8/fffs2LFCtauXUt4eDiJiYls2rSJGjVqkJaWxsiRIzExMaFt27ZMnDgRT09P+vbtC8CmTZsICgpSKRJT2SIjI/Hx8SEiIoJx48aV+v47d+6US4Ldi5LcCgJ0AKZNm0ZgYCA2NjYsWbKE6OhojI2NuXHjBrt27SI9PZ3hw4cTFRX1SudeFm/8wF67dm06dOhAixYtUCqVosa7ILyiqpjutnXrVr777juePXuGqakpK1asQKlU8vXXX3P//n1ycnKYNm0aN27cYPfu3SiVSsaPH09qaiqbN29GR0eHRo0aMWvWrCJRn8Xte+LEiQwZMoSOHTty8eJFVq1axbJly5g+fTq3bt1CqVTi5+dHp06dcHJyolGjRkUqy6WmpuLn54ednR1xcXEsW7YMQ0NDTExMaN68OePGjWPRokUkJCSgVCoZOnQo/fv3L7Gv7t+/L9cq37p1K1u2bJEHfVNTU8aOHcuOHTswNzfn4cOH8qAOyBnthUmSxNy5c/ntt9/Iyclh3LhxGBkZsXPnTvkLQEGy3pQpU0hPTyc9PZ3GjRvTsWNHBgwYQGpqKqNGjSIqKuqlzufOnTs8fvyYESNG4OLiwujRo0uNYd23b1+ZEuyWLFlCYmKiyrbQ0FC5lsCLktwKt/fBgwdyqqeNjQ3Hjx+nQYMGdO/eHQ0NDczMzNDU1CQ1NVWusFfe3siBPS4ujitXrjB69GgUCgUbNmwoc81pQRCqjpLS3fr37096ejqbNm1CQ0MDX19fLl68yMWLF6lfvz5Llizh5s2b/O9//8PY2BhjY2NWr15NWloaQUFB7NmzB0NDQ0JCQggPD8fb21s+rlKpLHbfbm5u7Nmzh44dOxIVFYW7uzuRkZGYmpoSEhJCWloa3t7eHDx4kMzMTMaMGUPLli05ffo0w4YNo1OnTiQmJrJ8+XK5hG14eDi1atWSQ1BOnDjB3bt32bFjB1lZWbi7u9O1a9ciISOfffYZWVlZpKSk0L17dznqNC0tTSW1DlST3grCYwpoamoWWYY/duyYnEL3+PFjNm7cSJcuXV7499S5c2eGDh3K9evXmTVrFgMGDGDfvn24uLiU+XwK7Nq1C1dXV4yNjWnbti0xMTFFInYLUygUZU6wmzBhwgv3Ay9OciusQYMGnD17lo4dOxIXF8ezZ89o0aIFGzduZPDgwfz5559cv36dZ8+elXisf+ONG9izs7OZMGECKSkpuLq6Ym5uLgZ1QSgHVS3dTUNDA21tbSZOnIi+vj5//vknubm5/P777/Lya6NGjRg6dChRUVFyMMydO3ewsrKSE8A6dOjAyZMnVWZzmzZtKnbf3bt3Z8GCBaSnp5OQkEBgYCCzZ8/m/PnzXLhwAYDc3FwePXoE/H/am7m5OatXr2bXrl0oFAr5PYaGhvIss3379jx8+JCrV6/yyy+/4OPjI+/v3r17RQbCgqX4xYsXc/fuXbn2vaGhIenp6dSsWVN+761bt6hbty716tUrkvSWk5PD4cOH+fjjj+VtN27coE2bNgCYmJjg5+fHmTNnVD5XuFp5wXlaWVmRl5fHvXv3OHToEJs2bSI8PLxM5wP5lT+jo6OpX78+sbGxPH78mK1bt+Lg4CAnvRVWkPZW3HkVl2BX2oz9RUluhYWEhDBnzhxWrlxJ+/bt0dHRoVu3bly8eBEfHx+aNm2KtbW1Sv+Xtzdm3fnvv/8G8mvxrlmzhsOHD1fYMoggCK9HSV/KL1++zLFjx/jvf//LtGnTUCqVSJIkp5RB/iBeMBMuuAz3zjvvkJycTGZmJgBnz56lcePGTJgwgbCwMMLCwrh27Vqx+9bQ0MDe3p4ZM2ZgZ2eHpqYmTZo0wdHRkbCwML799lvs7e3lf9QLjrl06VKcnZ1ZsGABnTp1QpIk3nrrLZ4+fSp/CUhKSgLyk9E6depEWFgYmzdvpn///sXmrRfw8/MjJSWF7du3A+Dt7U1wcLA8CP7111+sWLECT09P6tSpg6mpqUoc65YtWzh+/LjKPps0acIvv/wC5CfV+fr6UqNGDTnp7d69ezx+/LjYv6eBAweyYMECrKysMDY2fqnzOXHiBK1atSIsLIzQ0FB27drFX3/9xeXLl7G2tiY2NlaO0719+zbZ2dm89dZbODk5ERkZKfdlQYJdQXsLFP47LvivcEnfwklu2dnZJCQkyFG2hdtYUBY4PT2drl27cuPGDerWrcvOnTsZM2YMCoWiQmNc34gZ+5QpUzh06BAnT57E2NhYThgSBEF9NWzYED09PTw9PYH8WXFKSgqenp4EBATg7e1NXl4eAQEBXLt2Tf6cmZkZ48aNY8iQIWhoaGBhYcHkyZPLtG8AV1dX7OzsOHr0KJBf/zwwMBBvb28yMjLw8vIqci+Pvb098+fPZ926dbz99tukpaWhoaHBtGnTGDFiBEZGRiiVSho2bEjv3r05e/YsXl5eZGZmYmdnp5I5/k8Fd2R7e3tjZ2eHj48PeXl5DB48GC0tLRQKBWPGjJGvC8+fP59Zs2axYcMGcnJysLCwIDg4WGWfffr0IT4+nkGDBpGXl8fnn39Oq1atMDIyws3NDUtLyyJL+oXPdc6cOXLp3BedT8HNZS4uLvJnIyIicHNzU9nfwIED2bZtm7wy4uLigqGhIZIkMW/ePIAyJ9iVRltbu9gkt+vXr7N161ZmzJhBw4YNGTp0KHp6enTq1ImePXuSlZXF4sWL2b59OzVq1CAoKOiljvuy3oh0t7lz5xIdHc2WLVuwtLSsgBaqB3VIHasO1KGfRbrb67F27VqGDRuGjo4OkydPplu3bnzyySeV3Syg4vv48uXLXLp0Sc5aV2flne6mlkvxGRkZbNy4Ub7GM2nSJGJjY8WgLghCtWJgYIC7uzuenp5IklTiTWLqpmbNmkXuxhfKRi2X4v39/dmxYwdmZmY4OzuX+iiEIAhCVeTt7a1yN/6b5O23367sJlRbajOwF34G3d/fn3r16tGvX79KbpUgCIIglEySpHJ9OkstluJ//PFHunfvzpUrVwCwsLBg6tSp6OrqVnLLBEE9aWhoyHcfC4Lw7+Tl5ZVrcTS1mLE/fPiQK1eucPr0aZo3b17ZzREEtaelpcWzZ8/IzMxEU1OzytWCyMnJKfJMs1C+RB//e5IkkZeXR15enly3vzxU2xn7uXPn5Mo9Tk5O/PjjjwwbNqySWyUIbw4jIyN0dHSq3KAO+aU/hYol+vjfUygU6OjolHvATrWcsR8+fBhvb2/+85//yM9XWllZVXKrBOHNU56zjPJWVR/FUyeij6umCpuxK5VKgoKC8PDwwMfHh1u3bqm8HhERgYuLC+7u7sTFxb3Uvnv27Enfvn1xcnIqzyYLgiAIQrVXYV+3jx07RnZ2NuHh4fz888/MnTtXrjSUmppKWFgYu3fvJisrCy8vL7p27Vrqt7+4uDjs7e3R19dn586dFdV0QRAEQai2KmxgP3/+PN27dwegbdu2XLp0SX7twoULtGvXDh0dHXR0dLCwsODy5ctyqMA/FRSa2bNnD7169aqS1/TURVZWVmU34Y0g+rniiT6ueKKPK07BjYmvUhy2wgb2jIwMlfrFmpqa5ObmoqWlVabou8JycnIAGDlypBw8/uSJewAAColJREFUIFSMwl/AhIoj+rniiT6ueKKPK15OTs5LP7pdYQP7P+PtlEqlfKNNWaLvCjMwMKBZs2Zoa2uL2bogCIKg9iRJIicn55Xq8VfYwG5jY0NcXBwODg78/PPPNGvWTH6tTZs2/Pe//yUrK4vs7GySk5NVXv8nDQ2Ncn8cQBAEQRCqslctslZh6W5KpZIZM2Zw9epVJEkiJCSE+Ph4LCws6NOnDxEREYSHhyNJEqNGjRLlXwVBEAShHFSL2FZBEARBEMqm2laeEwRBEAShKDGwC4IgCIIaqXIDe0VWrBPyldbHmzZtws3NDTc3N1asWFFJrazeSuvjgvcMHz6cHTt2VEILq7/S+vjEiRO4u7vj5ubGjBkzXul5YKH0ft6wYQMuLi64uroSExNTSa1UD0lJSfj4+BTZHhsbi6urKx4eHkRERJS+I6mKOXr0qOTv7y9JkiT99NNP0ujRo+XXUlJSJCcnJykrK0v6+++/5Z+Fl1NSH9++fVsaMGCAlJubKymVSsnDw0P67bffKqup1VZJfVxg0aJFkpubm7R9+/bX3Ty1UFIfP3nyRHJ0dJT++usvSZIkad26dfLPwsspqZ8fP34s9ezZU8rKypLS09OlXr16VVYzq71169ZJTk5Okpubm8r27Oxsyc7OTkpPT5eysrIkFxcXKTU1tcR9VbkZe1kr1hkZGckV64SXU1Ifv/3226xfv16O4szNzaVGjRqV1dRqq6Q+Bjhy5AgKhUJ+j/DySurjn376iWbNmjFv3jy8vLyoVasWZmZmldXUaq2kftbT06NevXo8e/aMZ8+eiToj/4KFhQXLly8vsj05ORkLCwtMTEzQ0dHh/fff59y5cyXuq8pFM5VnxTqheCX1sba2NmZmZkiSxPz582nZsiWNGzeuxNZWTyX18dWrVzlw4ADLli1j5cqVldjK6q2kPk5LS+PMmTPs3bsXfX19Bg8eTNu2bcXv8isoqZ8B6tati6OjI3l5eYwaNaqymlnt9evXj7t37xbZ/irjXpUb2MuzYp1QvJL6GPLrPwcEBGBgYMD06dMro4nVXkl9vHfvXh48eMCnn37KvXv30NbWpn79+vTo0aOymlstldTHNWvWpHXr1pibmwPQvn17fvvtNzGwv4KS+jk+Pp6UlBSOHz8OgK+vLzY2Ni/M/RBe3quMe1VuKd7Gxob4+HiAYivWnT9/nqysLJ48eVJqxTqheCX1sSRJjBkzhubNmzNr1iw0NTUrq5nVWkl9/NVXXxEZGUlYWBgDBgxg6NChYlB/BSX1sbW1NVevXuXRo0fk5uaSlJSElZVVZTW1Wiupn01MTNDV1UVHR4caNWpgZGTE33//XVlNVUuWlpbcunWL9PR0srOzSUhIoF27diV+psrN2Pv27cupU6fw9PSUK9Zt3LhRrljn4+ODl5cXkiQxYcIEcf33FZTUx0qlkrNnz5Kdnc33338PwMSJE0v9RRJUlfZ7LPx7pfXxpEmTGD58OAD29vZiEvCKSuvn06dP4+7ujoaGBjY2NnTt2rWym6wWoqOjyczMxMPDgylTpuDr64skSbi6ulKnTp0SPysqzwmCIAiCGqlyS/GCIAiCILw6MbALgiAIghoRA7sgCIIgqBExsAuCIAiCGhEDuyAIgiCokSr3uJsgqKO7d+9ib2+PpaWlyvY1a9ZQt27dYj9TUF5y3Lhxr3zcqKgo5s6dKx/j+fPndOzYkenTp6sUJSqLpUuX0qpVK/mx07CwMACcnZ3Zt2/fK7cRwMfHhz///BN9fX0gv9pWgwYNWLhwIbVq1Xrh58LDwzEwMMDJyelfHV8Q1IkY2AXhNaldu/a/HgBfRe/evZk7dy4AeXl5+Pj4sG3bNj799NOX2s8XX3wh/3z27Fn55/I6p+DgYDp16gTkVzcbP348Gzdu5Msvv3zhZ3766Sc6duxYLscXBHUhBnZBqGRXr15l9uzZZGZm8ujRI4YNG8aQIUPk13NycggICODatWsAeHl54e7uzsOHDwkKCuLPP/9EoVAwadIkbG1tSzyWpqYm7dq14+bNmwDs3r2bjRs3olAosLa2Ztq0aejo6BR7vClTptCxY0d+/fVXANzc3IiMjKR58+b88ssv9OrVi71791KrVi3S09NxcnIiLi6OH374gWXLlpGbm8s777zD7NmzMTU1LbGdmZmZpKWlyaVJDx8+zMaNG3n+/DlZWVkEBweTk5NDbGwsP/74I+bm5rRo0eKl+0MQ1JG4xi4Ir0lKSgrOzs7yf+vXrwcgMjKSMWPGsHv3brZs2cKSJUtUPvfTTz/x+PFj9u7dy8aNG0lMTARgzpw5uLq6EhUVxerVqwkKCio1HCItLY34+HhsbGy4cuUKa9asISwsjOjoaPT09FixYsULj1cgMDBQbncBLS0t7O3tOXLkCADfffcddnZ2PHnyhEWLFhEaGsrevXvp1q0bCxcuLLZtgYGBfPzxx3Tr1g0PDw9sbW0ZOnQoSqWSnTt3smbNGvbv38+IESMIDQ3F1taW3r17M378eLp37/5K/SEI6kjM2AXhNXnRUvyUKVP4/vvvWbt2LVeuXCEzM1Pl9aZNm3Ljxg18fX3p0aMHkydPBuD06dP8/vvvLFu2DIDc3Fzu3LlDixYtVD4fGxuLs7MzkiQhSRJ9+/bFycmJbdu28cEHH8izZw8PD77++mtGjhxZ7PFK4+zsTEhICN7e3hw4cAA/Pz+SkpL4448/5BUIpVKJiYlJsZ8vWIpPTExk/Pjx9OzZEx0dHQBWrlxJbGwsN27c4OzZs2hoFJ2TlLU/BEHdiYFdECqZn58fxsbGfPDBBzg4OHDw4EGV101NTTl48CCnTp3ixIkTDBgwgIMHD6JUKtm8eTM1a9YE4MGDB8XeaFb4GnthSqVS5c+SJJGbm/vC45WmdevWPH78mAsXLvDgwQNsbGw4duwYNjY2rFmzBshPDiycVFUcGxsbfHx88Pf3Z9++fWRlZeHq6oqzszMdOnSgefPmbNu2rdjzKUt/CIK6E0vxglDJTp06xfjx47Gzs+PcuXNA/k1uBY4fP87kyZPp1asXgYGB6Ovr88cff9C5c2e2b98OwPXr1/n444959uxZmY/bsWNHYmNjSU9PByAiIoJOnTq98HiFFWRy/9NHH33E9OnTcXBwAOC9997j559/5saNGwCsWrWK+fPnl9q2YcOG8ezZM3bu3MnNmzfR0NBg9OjRdO7cmfj4eLl/NDU15Z//bX8IgroQM3ZBqGTjxo3Dy8sLY2NjGjduTP369bl79678eo8ePTh69CiOjo7UqFGDDz/8kObNmxMYGEhQUBAfffQRAPPnz8fQ0LDMx3333XcZNWoUPj4+5OTkYG1tzcyZM6lRo0axxyusT58+ODs7ExUVpbL9448/ZunSpSxevBgAc3NzQkJC8PPzQ6lUUqdOHRYsWFBq23R0dPDz8yMkJISYmBhatGhB//790dXVpUOHDty/fx8AW1tbFi9ejJGR0b/uD0FQFyLdTRAEQRDUiFiKFwRBEAQ1IgZ2QRAEQVAjYmAXBEEQBDUiBnZBEARBUCNiYBcEQRAENSIGdkEQBEFQI2JgFwRBEAQ1IgZ2QRAEQVAj/wd4doNeXvBPMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifiers = [\n",
    "    BernoulliNB(),\n",
    "    MultinomialNB(),\n",
    "    LogisticRegression(),\n",
    "    LogisticRegressionCV()\n",
    "]\n",
    "\n",
    "for classifier in classifiers:\n",
    "    oz = ROCAUC(classifier)\n",
    "    oz.fit(X_train, y_train)\n",
    "    oz.score(X_test, y_test)\n",
    "    g = oz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-fdd6649d3baa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLinearSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "oz = ROCAUC(LinearSVC())\n",
    "oz.fit(X_train, y_train)\n",
    "oz.score(X_test, y_test)\n",
    "oz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-c368b2b64b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "oz = ROCAUC(SVC())\n",
    "oz.fit(X_train, y_train)\n",
    "oz.score(X_test, y_test)\n",
    "oz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-40e9b8ff5b15>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSGDClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "oz = ROCAUC(SGDClassifier())\n",
    "oz.fit(X_train, y_train)\n",
    "oz.score(X_test, y_test)\n",
    "oz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-9df2d7d6a412>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPassiveAggressiveClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "oz = ROCAUC(PassiveAggressiveClassifier())\n",
    "oz.fit(X_train, y_train)\n",
    "oz.score(X_test, y_test)\n",
    "oz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-11e58667c1dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRidgeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "oz = ROCAUC(RidgeClassifier())\n",
    "oz.fit(X_train, y_train)\n",
    "oz.score(X_test, y_test)\n",
    "oz.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-fb85c788aacd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRidgeClassifierCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0moz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0;31m# Compute ROC curve and ROC area for each class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "oz = ROCAUC(RidgeClassifierCV())\n",
    "oz.fit(X_train, y_train)\n",
    "oz.score(X_test, y_test)\n",
    "oz.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so what's going on here?\n",
    "\n",
    "### The Shape of `y_pred`\n",
    "\n",
    "It looks like all of the classifiers that trigger the `IndexError` during binary classification with `ROCAUC` are ones that have only a `decision_function` and for which `y_pred.shape` is (n_samples,).\n",
    "\n",
    "#### Classifiers that Raise the IndexError with Binary Classification & `ROCAUC`\n",
    "\n",
    " - [LinearSVC()](http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC.decision_function)\n",
    " - [SVC()](http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.decision_function)\n",
    " - [SGDClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier.decision_function)\n",
    " - [PassiveAggressiveClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier.decision_function)\n",
    " - [RidgeClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html#sklearn.linear_model.RidgeClassifier.decision_function)\n",
    " - [RidgeClassifierCV()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifierCV.html#sklearn.linear_model.RidgeClassifierCV.decision_function)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "attrs = (\n",
    "    'predict_proba',\n",
    "    'decision_function',\n",
    ")\n",
    "\n",
    "failing_classifiers = [\n",
    "    LinearSVC(),                   \n",
    "    SVC(),                     \n",
    "    SGDClassifier(),              \n",
    "    PassiveAggressiveClassifier(), \n",
    "    RidgeClassifier(),             \n",
    "    RidgeClassifierCV()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_pred shape for LinearSVC is (4112,).\n",
      "[-2.07818848 -1.10295589 -2.65880378 ... -1.5675887  -2.22618865\n",
      " -2.21660691]\n",
      "y_pred shape for SVC is (4112,).\n",
      "[-1.0042836  -1.04618017 -1.01206048 ... -0.50206514 -0.9921702\n",
      " -0.75343404]\n",
      "y_pred shape for SGDClassifier is (4112,).\n",
      "[-119987.75965156  -72882.79935914 -107597.19061436 ...  -68765.52714843\n",
      " -113531.54391398  -36124.36901115]\n",
      "y_pred shape for PassiveAggressiveClassifier is (4112,).\n",
      "[-2.29768419 -1.21982115 -2.12816317 ... -1.21089063 -2.20835107\n",
      " -1.04096801]\n",
      "y_pred shape for RidgeClassifier is (4112,).\n",
      "[-0.97824702 -0.72364345 -1.14475694 ... -0.79729413 -0.91117693\n",
      " -0.8346179 ]\n",
      "y_pred shape for RidgeClassifierCV is (4112,).\n",
      "[-0.97826157 -0.72362511 -1.14477574 ... -0.7972869  -0.91116793\n",
      " -0.83472035]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n",
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.passive_aggressive.PassiveAggressiveClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "def profile(classifiers):\n",
    "    for classifier in classifiers:\n",
    "        classifier.fit(X_train, y_train)\n",
    "        # Return the first resolved function\n",
    "        for attr in attrs:\n",
    "            try:\n",
    "                method = getattr(classifier, attr, None)\n",
    "                if method:\n",
    "                    y_pred = method(X_test)\n",
    "            except AttributeError:\n",
    "                continue\n",
    "        print(\"y_pred shape for {} is {}.\".format(\n",
    "            classifier.__class__.__name__, y_pred.shape)\n",
    "        ) \n",
    "        print(y_pred)\n",
    "\n",
    "profile(failing_classifiers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classifiers that Currently Work with Binary Classification & `ROCAUC`\n",
    "\n",
    "The classifiers with decision functions and `y_pred.shape` (n_samples, ) that *do* work with ROCAUC for binary classification seem to work because they also have `predict_proba`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_pred shape for AdaBoostClassifier is (4112,).\n",
      "[-0.89443802 -0.75144235 -0.92373965 ... -0.74635979 -0.93102191\n",
      " -0.25120968]\n",
      "y_pred shape for QuadraticDiscriminantAnalysis is (4112,).\n",
      "[-25.52942121 -20.63607659 -21.32444067 ... -17.48076674 -15.83153313\n",
      " -27.96407765]\n",
      "y_pred shape for LogisticRegression is (4112,).\n",
      "[-7.21761368 -4.18943292 -9.03165307 ... -5.61892766 -7.64128122\n",
      " -7.31392629]\n",
      "y_pred shape for LogisticRegressionCV is (4112,).\n",
      "[-8.12155049 -4.86393333 -8.83520012 ... -5.89258174 -8.74651903\n",
      " -5.72819053]\n"
     ]
    }
   ],
   "source": [
    "working_classifiers_decision = [\n",
    "    AdaBoostClassifier(),\n",
    "    QuadraticDiscriminantAnalysis(),\n",
    "    LogisticRegression(),\n",
    "    LogisticRegressionCV()\n",
    "]\n",
    "\n",
    "profile(working_classifiers_decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sklearn Documentation\n",
    "\n",
    " - [AdaBoostClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier.decision_function)\n",
    " - [QuadraticDiscriminantAnalysis()](http://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function)\n",
    " - [LogisticRegression()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.decision_function)\n",
    " - [LogisticRegressionCV()](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV.decision_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_pred shape for MLPClassifier is (4112, 2).\n",
      "[[9.99999821e-01 1.78587267e-07]\n",
      " [9.99999822e-01 1.77711818e-07]\n",
      " [9.99999416e-01 5.84020242e-07]\n",
      " ...\n",
      " [9.99999441e-01 5.59388516e-07]\n",
      " [9.99999211e-01 7.88712395e-07]\n",
      " [9.99264783e-01 7.35217361e-04]]\n",
      "y_pred shape for DecisionTreeClassifier is (4112, 2).\n",
      "[[1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " ...\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n",
      "y_pred shape for RandomForestClassifier is (4112, 2).\n",
      "[[1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " ...\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n",
      "y_pred shape for BernoulliNB is (4112, 2).\n",
      "[[9.99902348e-01 9.76522911e-05]\n",
      " [9.99902348e-01 9.76522911e-05]\n",
      " [9.99902348e-01 9.76522911e-05]\n",
      " ...\n",
      " [9.99902348e-01 9.76522911e-05]\n",
      " [9.99902348e-01 9.76522911e-05]\n",
      " [3.93484879e-01 6.06515121e-01]]\n",
      "y_pred shape for MultinomialNB is (4112, 2).\n",
      "[[1.00000000e+000 1.08610234e-097]\n",
      " [1.00000000e+000 1.29784432e-231]\n",
      " [1.00000000e+000 3.25319193e-078]\n",
      " ...\n",
      " [1.00000000e+000 5.25817964e-196]\n",
      " [1.00000000e+000 1.04606462e-075]\n",
      " [2.93149511e-065 1.00000000e+000]]\n"
     ]
    }
   ],
   "source": [
    "working_classifiers_proba = [\n",
    "    MLPClassifier(),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    BernoulliNB(),\n",
    "    MultinomialNB()\n",
    "]\n",
    "\n",
    "profile(working_classifiers_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sklearn Documentation\n",
    " \n",
    " - [MLPClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html#sklearn.neural_network.MLPClassifier.predict_proba)\n",
    " - [DecisionTreeClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.predict_proba)\n",
    " - [RandomForestClassifier()](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.predict_proba)\n",
    " - [BernoulliNB()](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB.predict_proba)\n",
    " - [MultinomialNB()](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB.predict_proba)\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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